





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
				import os

				import jax.numpy as jnp
				from jax import jit

				from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
				from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

				UpperCAmelCase		:  Dict         =	'''0.12'''  # assumed parallelism: 8


if is_torch_available():
				import torch







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a=None    ) -> Any:
					if rng is None:
										__A      :		Tuple         =							random.Random()

					__A      :		Optional[int]         =							1
					for dim in shape:
										total_dims *= dim

					__A      :		List[str]         =							[]
					for _ in range(a_    ):
										values.append(rng.randint(0   ,		vocab_size - 1    )    )

					__A      :		Tuple         =							np.array(a_   ,		dtype=jnp.intaa    ).reshape(a_    )

					return output







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a=None    ) -> List[Any]:
					__A      :		str         =							ids_tensor(a_   ,		vocab_size=2   ,		rng=a_    )
					# make sure that at least one token is attended to for each batch
					__A      :		Dict         =							1
					return attn_mask





@require_flax
class 		_A:


				"""simple docstring"""


				UpperCamelCase       :    Tuple          =      None
				UpperCamelCase       :    Optional[int]          =      ()



				def    UpperCAmelCase_						(     self	):
									__A      :		List[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									__A      :		Dict         =							2
									__A      :		str         =							inputs["input_ids"].shape[-1] // 2
									__A      :		Tuple         =							inputs["input_ids"][:max_batch_size, :sequence_length]

									__A      :		str         =							jnp.ones_like(a_	)
									__A      :		Union[str, Any]         =							attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									__A      :		int         =							input_ids.shape[-1] + 5
									if config.eos_token_id is not None and config.pad_token_id is None:
														# hack to allow generate for models such as GPT2 as is done in `generate()`
														__A      :		Tuple         =							config.eos_token_id
									return config, input_ids, attention_mask, max_length



				@is_pt_flax_cross_test
				def    UpperCAmelCase_						(     self	):
									__A      :		Any         =							self._get_input_ids_and_config()
									__A      :		List[Any]         =							False
									__A      :		Optional[Any]         =							max_length
									__A      :		Tuple         =							0

									for model_class in self.all_generative_model_classes:
														__A      :		List[str]         =							model_class(a_	)

														__A      :		int         =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
														__A      :		Any         =							getattr(a_	,   a_	)
														__A      :		str         =							pt_model_class(a_	).eval()
														__A      :		str         =							load_flax_weights_in_pytorch_model(a_	,   flax_model.params	)

														__A      :		Dict         =							flax_model.generate(a_	).sequences
														__A      :		str         =							pt_model.generate(torch.tensor(a_	,   dtype=torch.long	)	)

														if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																			__A      :		Optional[int]         =							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

														self.assertListEqual(pt_generation_outputs.numpy().tolist()	,   flax_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		List[str]         =							self._get_input_ids_and_config()
									__A      :		int         =							False
									__A      :		int         =							max_length

									for model_class in self.all_generative_model_classes:
														__A      :		int         =							model_class(a_	)

														__A      :		Dict         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		str         =							jit(model.generate	)
														__A      :		Dict         =							jit_generate(a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		List[str]         =							self._get_input_ids_and_config()
									__A      :		Any         =							True
									__A      :		Optional[Any]         =							max_length

									for model_class in self.all_generative_model_classes:
														__A      :		List[Any]         =							model_class(a_	)

														__A      :		Tuple         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		Dict         =							jit(model.generate	)
														__A      :		Dict         =							jit_generate(a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		List[Any]         =							self._get_input_ids_and_config()
									__A      :		Any         =							False
									__A      :		Dict         =							max_length
									__A      :		Union[str, Any]         =							2

									for model_class in self.all_generative_model_classes:
														__A      :		List[str]         =							model_class(a_	)

														__A      :		Optional[int]         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		List[str]         =							jit(model.generate	)
														__A      :		List[str]         =							jit_generate(a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							self._get_input_ids_and_config()
									__A      :		int         =							False
									__A      :		Dict         =							max_length
									__A      :		Dict         =							2
									__A      :		Optional[int]         =							2

									for model_class in self.all_generative_model_classes:
														__A      :		Any         =							model_class(a_	)

														__A      :		Optional[int]         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[0]	,   input_ids.shape[0] * config.num_return_sequences	)



				def    UpperCAmelCase_						(     self	):
									__A      :		str         =							self._get_input_ids_and_config()
									__A      :		Tuple         =							True
									__A      :		str         =							max_length
									__A      :		Union[str, Any]         =							0.8
									__A      :		List[Any]         =							10
									__A      :		Tuple         =							0.3
									__A      :		Any         =							1
									__A      :		str         =							8
									__A      :		List[Any]         =							9

									for model_class in self.all_generative_model_classes:
														__A      :		str         =							model_class(a_	)

														__A      :		str         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		List[str]         =							jit(model.generate	)
														__A      :		int         =							jit_generate(a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		str         =							self._get_input_ids_and_config()
									__A      :		List[str]         =							max_length
									__A      :		Optional[Any]         =							1
									__A      :		Any         =							8
									__A      :		Any         =							9

									for model_class in self.all_generative_model_classes:
														__A      :		Tuple         =							model_class(a_	)

														__A      :		str         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		Optional[int]         =							jit(model.generate	)
														__A      :		Optional[int]         =							jit_generate(a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Any         =							self._get_input_ids_and_config()
									__A      :		Tuple         =							max_length
									__A      :		Dict         =							2
									__A      :		str         =							1
									__A      :		Tuple         =							8
									__A      :		str         =							9

									for model_class in self.all_generative_model_classes:
														__A      :		Dict         =							model_class(a_	)

														__A      :		List[Any]         =							model.generate(a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		List[str]         =							jit(model.generate	)
														__A      :		Union[str, Any]         =							jit_generate(a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Tuple         =							self._get_input_ids_and_config()

									# pad attention mask on the left
									__A      :		List[Any]         =							attention_mask.at[(0, 0)].set(0	)

									__A      :		Tuple         =							False
									__A      :		List[str]         =							max_length

									for model_class in self.all_generative_model_classes:
														__A      :		str         =							model_class(a_	)

														__A      :		Optional[int]         =							model.generate(a_	,   attention_mask=a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		Union[str, Any]         =							jit(model.generate	)
														__A      :		Tuple         =							jit_generate(a_	,   attention_mask=a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							self._get_input_ids_and_config()

									# pad attention mask on the left
									__A      :		Union[str, Any]         =							attention_mask.at[(0, 0)].set(0	)

									__A      :		str         =							True
									__A      :		Dict         =							max_length

									for model_class in self.all_generative_model_classes:
														__A      :		Tuple         =							model_class(a_	)

														__A      :		int         =							model.generate(a_	,   attention_mask=a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		int         =							jit(model.generate	)
														__A      :		Tuple         =							jit_generate(a_	,   attention_mask=a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)





				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[int]         =							self._get_input_ids_and_config()

									# pad attention mask on the left
									__A      :		Optional[int]         =							attention_mask.at[(0, 0)].set(0	)

									__A      :		List[Any]         =							2
									__A      :		str         =							max_length

									for model_class in self.all_generative_model_classes:
														__A      :		Optional[Any]         =							model_class(a_	)

														__A      :		Any         =							model.generate(a_	,   attention_mask=a_	).sequences
														self.assertEqual(generation_outputs.shape[-1]	,   a_	)

														__A      :		List[str]         =							jit(model.generate	)
														__A      :		Optional[Any]         =							jit_generate(a_	,   attention_mask=a_	).sequences

														self.assertListEqual(generation_outputs.tolist()	,   jit_generation_outputs.tolist()	)





@require_flax
class 		_A(   unittest.TestCase  ):


				"""simple docstring"""


				def    UpperCAmelCase_						(     self	):
									__A      :		int         =							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'	)
									__A      :		List[str]         =							FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'	)

									__A      :		Optional[int]         =							"Hello world"
									__A      :		Any         =							tokenizer(a_	,   return_tensors='np'	).input_ids

									# typos are quickly detected (the correct argument is `do_sample`)
									with self.assertRaisesRegex(a_	,   'do_samples'	):
														model.generate(a_	,   do_samples=a_	)

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(a_	,   'foo'	):
														__A      :		Optional[int]         =							{"foo": "bar"}
														model.generate(a_	,   **a_	)











'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCAmelCase									=   """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)






def   __A    (				a_    :	Dict   ,a_    :	List[Any]   ,a_    :	List[str]=None   ,a_    :	Optional[int]=None   ,a_    :	Any=None   ,a_    :	Any=None   ,a_    :	str=None   ,a_    :	Union[str, Any]=None   ,):
		if attention_mask is None:
				lowerCAmelCase  :     List[Any]       						= np.where(input_ids != config.pad_token_id   ,1   ,0						)
		if decoder_attention_mask is None:
				lowerCAmelCase  :     Union[str, Any]       						= np.where(decoder_input_ids != config.pad_token_id   ,1   ,0						)
		if head_mask is None:
				lowerCAmelCase  :     Dict       						= np.ones((config.encoder_layers, config.encoder_attention_heads)						)
		if decoder_head_mask is None:
				lowerCAmelCase  :     int       						= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		if cross_attn_head_mask is None:
				lowerCAmelCase  :     Union[str, Any]       						= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_=13		,   a_=7		,   a_=True		,   a_=False		,   a_=99		,   a_=16		,   a_=2		,   a_=4		,   a_=4		,   a_="gelu"		,   a_=0.1		,   a_=0.1		,   a_=32		,   a_=2		,   a_=1		,   a_=0		,   a_=0.02		,   ):
			lowerCAmelCase  :     Optional[int]       						= parent
			lowerCAmelCase  :     Tuple       						= batch_size
			lowerCAmelCase  :     Any       						= seq_length
			lowerCAmelCase  :     int       						= is_training
			lowerCAmelCase  :     int       						= use_labels
			lowerCAmelCase  :     Any       						= vocab_size
			lowerCAmelCase  :     Tuple       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     int       						= intermediate_size
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     List[Any]       						= hidden_dropout_prob
			lowerCAmelCase  :     List[str]       						= attention_probs_dropout_prob
			lowerCAmelCase  :     Dict       						= max_position_embeddings
			lowerCAmelCase  :     Optional[int]       						= eos_token_id
			lowerCAmelCase  :     Union[str, Any]       						= pad_token_id
			lowerCAmelCase  :     Tuple       						= bos_token_id
			lowerCAmelCase  :     Tuple       						= initializer_range



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size						)		,   3		,   self.vocab_size						)
			lowerCAmelCase  :     str       						= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,   dtype=np.intaa						))		,   -1						)

			lowerCAmelCase  :     List[str]       						= shift_tokens_right(a_		,   1		,   2						)

			lowerCAmelCase  :     Union[str, Any]       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   encoder_layers=self.num_hidden_layers		,   decoder_layers=self.num_hidden_layers		,   encoder_attention_heads=self.num_attention_heads		,   decoder_attention_heads=self.num_attention_heads		,   encoder_ffn_dim=self.intermediate_size		,   decoder_ffn_dim=self.intermediate_size		,   dropout=self.hidden_dropout_prob		,   attention_dropout=self.attention_probs_dropout_prob		,   max_position_embeddings=self.max_position_embeddings		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.bos_token_id		,   pad_token_id=self.pad_token_id		,   initializer_range=self.initializer_range		,   use_cache=a_		,   )
			lowerCAmelCase  :     Tuple       						= prepare_blenderbot_inputs_dict(a_		,   a_		,   a_						)
			return config, inputs_dict



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= self.prepare_config_and_inputs()
			return config, inputs_dict



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Any       						= 20
			lowerCAmelCase  :     int       						= model_class_name(a_						)

			lowerCAmelCase  :     Tuple       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     Tuple       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype="i4"						)

			lowerCAmelCase  :     Dict       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )
			lowerCAmelCase  :     Union[str, Any]       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     Tuple       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Optional[Any]       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     int       						= model.decode(a_		,   a_						)

			lowerCAmelCase  :     List[str]       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= 20
			lowerCAmelCase  :     Tuple       						= model_class_name(a_						)

			lowerCAmelCase  :     Dict       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     str       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     Any       						= jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
			    ]		,   axis=-1		,   )

			lowerCAmelCase  :     Union[str, Any]       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )

			lowerCAmelCase  :     Any       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )
			lowerCAmelCase  :     int       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Any       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     List[str]       						= model.decode(a_		,   a_		,   decoder_attention_mask=a_						)

			lowerCAmelCase  :     int       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)






@require_flax
class 		lowerCamelCase	(   unittest.TestCase							):
	snake_case_   = 99



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]		,   dtype=np.intaa		,   )

			lowerCAmelCase  :     Union[str, Any]       						= input_ids.shape[0]
			lowerCAmelCase  :     Dict       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=24		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=32		,   decoder_ffn_dim=32		,   max_position_embeddings=48		,   eos_token_id=2		,   pad_token_id=1		,   bos_token_id=0		,   )
			return config, input_ids, batch_size



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self._get_config_and_data()
			lowerCAmelCase  :     Tuple       						= FlaxBlenderbotForConditionalGeneration(a_						)
			lowerCAmelCase  :     str       						= lm_model(input_ids=a_						)
			lowerCAmelCase  :     Tuple       						= (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs["logits"].shape		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=14		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=8		,   decoder_ffn_dim=8		,   max_position_embeddings=48		,   )
			lowerCAmelCase  :     int       						= FlaxBlenderbotForConditionalGeneration(a_						)
			lowerCAmelCase  :     Optional[Any]       						= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     List[str]       						= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     List[str]       						= lm_model(input_ids=a_		,   decoder_input_ids=a_						)
			lowerCAmelCase  :     List[str]       						= (*summary.shape, config.vocab_size)
			self.assertEqual(outputs["logits"].shape		,   a_						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     Dict       						= shift_tokens_right(a_		,   1		,   2						)
			lowerCAmelCase  :     Optional[Any]       						= np.equal(a_		,   1						).astype(np.floataa						).sum()
			lowerCAmelCase  :     Any       						= np.equal(a_		,   1						).astype(np.floataa						).sum()
			self.assertEqual(shifted.shape		,   input_ids.shape						)
			self.assertEqual(a_		,   n_pad_before - 1						)
			self.assertTrue(np.equal(shifted[:, 0]		,   2						).all()						)






@require_flax
class 		lowerCamelCase	(   _A   , unittest.TestCase   , _A							):
	snake_case_   = True
	snake_case_   = (
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	snake_case_   = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= FlaxBlenderbotModelTester(self						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     List[str]       						= self._prepare_for_class(a_		,   a_						)
							lowerCAmelCase  :     str       						= model_class(a_						)

							@jax.jit
							def encode_jitted(a_		,   a_=None		,   **a_						):
									return model.encode(input_ids=a_		,   attention_mask=a_						)

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     List[Any]       						= encode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Tuple       						= encode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     int       						= model_class(a_						)
							lowerCAmelCase  :     Optional[int]       						= model.encode(inputs_dict["input_ids"]		,   inputs_dict["attention_mask"]						)

							lowerCAmelCase  :     Optional[Any]       						= {
							    "decoder_input_ids": inputs_dict["decoder_input_ids"],
							    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
							    "encoder_outputs": encoder_outputs,
							}

							@jax.jit
							def decode_jitted(a_		,   a_		,   a_						):
									return model.decode(
									    decoder_input_ids=a_		,   decoder_attention_mask=a_		,   encoder_outputs=a_		,   )

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     str       						= decode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Optional[Any]       						= decode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	@slow
	def 	_lowerCamelCase     ( self						):
			for model_class_name in self.all_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class_name.from_pretrained("facebook/blenderbot-400M-distill"						)
					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
					lowerCAmelCase  :     Union[str, Any]       						= np.ones((1, 1)						) * model.config.eos_token_id
					lowerCAmelCase  :     Dict       						= model(a_						)
					self.assertIsNotNone(a_						)





	@unittest.skipUnless(jax_device != "cpu"		,   "3B test too slow on CPU."						)
	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
			lowerCAmelCase  :     Any       						= {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

			lowerCAmelCase  :     Optional[int]       						= FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,   from_pt=a_						)
			lowerCAmelCase  :     List[str]       						= BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"						)

			lowerCAmelCase  :     Any       						= ["Sam"]
			lowerCAmelCase  :     int       						= tokenizer(a_		,   return_tensors="jax"						)

			lowerCAmelCase  :     List[Any]       						= model.generate(**a_		,   **a_						)
			lowerCAmelCase  :     Optional[Any]       						= "Sam is a great name. It means \"sun\" in Gaelic."

			lowerCAmelCase  :     Optional[Any]       						= tokenizer.batch_decode(a_		,   **a_						)
			assert generated_txt[0].strip() == tgt_text



